library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
library(TH.data)
library(psych)
library(whitening)
library("vioplot")
library("rpart")
library(mlbench)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
Source W. Nick Street, Olvi L. Mangasarian and William H. Wolberg (1995). An inductive learning approach to prognostic prediction. In A. Prieditis and S. Russell, editors, Proceedings of the Twelfth International Conference on Machine Learning, pages 522–530, San Francisco, Morgan Kaufmann.
Peter Buehlmann and Torsten Hothorn (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477–505.
wpbc {TH.data}
data("wpbc", package = "TH.data")
table(wpbc[,"status"])
#>
#> N R
#> 151 47
sum(1*(wpbc[,"status"]=="R" & wpbc$time <= 24))
#> [1] 29
wpbc <- subset(wpbc,time > 36 | status=="R" )
summary(wpbc$time)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.00 36.75 60.50 58.79 78.75 125.00
wpbc[,"status"] <- 1*(wpbc[,"status"]=="R")
wpbc <- wpbc[complete.cases(wpbc),]
pander::pander(table(wpbc[,"status"]))
| 0 | 1 |
|---|---|
| 91 | 46 |
wpbc$time <- NULL
studyName <- "Wisconsin"
dataframe <- wpbc
outcome <- "status"
thro <- 0.4
TopVariables <- 10
cexheat = 0.25
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 137 | 32 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 91 | 46 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) >= 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9961379
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> mean_perimeter mean_texture tsize
#> mean_radius mean_texture mean_perimeter mean_area
#> 0.96875 0.50000 1.00000 0.93750
#> mean_smoothness mean_compactness
#> 0.34375 0.40625
#>
#> Included: 32 , Uni p: 0.0046875 , Base Size: 3 , Rcrit: 0.2212374
#>
#>
1 <R=0.996,thr=0.950>, Top: 3< 2 >[Fa= 3 ]( 3 , 6 , 0 ),<|><>Tot Used: 9 , Added: 6 , Zero Std: 0 , Max Cor: 0.922
#>
2 <R=0.922,thr=0.900>, Top: 1< 1 >[Fa= 3 ]( 1 , 1 , 3 ),<|><>Tot Used: 9 , Added: 1 , Zero Std: 0 , Max Cor: 0.891
#>
3 <R=0.891,thr=0.800>, Top: 6< 1 >[Fa= 9 ]( 6 , 6 , 3 ),<|><>Tot Used: 18 , Added: 6 , Zero Std: 0 , Max Cor: 0.842
#>
4 <R=0.842,thr=0.800>, Top: 1< 1 >[Fa= 10 ]( 1 , 1 , 9 ),<|><>Tot Used: 20 , Added: 1 , Zero Std: 0 , Max Cor: 0.789
#>
5 <R=0.789,thr=0.700>, Top: 6< 1 >[Fa= 12 ]( 6 , 6 , 10 ),<|><>Tot Used: 25 , Added: 6 , Zero Std: 0 , Max Cor: 0.743
#>
6 <R=0.743,thr=0.700>, Top: 2< 1 >[Fa= 13 ]( 2 , 2 , 12 ),<|><>Tot Used: 27 , Added: 2 , Zero Std: 0 , Max Cor: 0.698
#>
7 <R=0.698,thr=0.600>, Top: 3< 2 >[Fa= 13 ]( 3 , 4 , 13 ),<|><>Tot Used: 27 , Added: 4 , Zero Std: 0 , Max Cor: 0.768
#>
8 <R=0.768,thr=0.700>, Top: 2< 1 >[Fa= 13 ]( 2 , 2 , 13 ),<|><>Tot Used: 27 , Added: 2 , Zero Std: 0 , Max Cor: 0.600
#>
9 <R=0.600,thr=0.600>, Top: 1< 1 >[Fa= 13 ]( 1 , 1 , 13 ),<|><>Tot Used: 27 , Added: 1 , Zero Std: 0 , Max Cor: 0.749
#>
10 <R=0.749,thr=0.700>, Top: 1< 1 >[Fa= 13 ]( 1 , 1 , 13 ),<|><>Tot Used: 27 , Added: 1 , Zero Std: 0 , Max Cor: 0.600
#>
11 <R=0.600,thr=0.500>, Top: 7< 1 >[Fa= 15 ]( 7 , 8 , 13 ),<|><>Tot Used: 28 , Added: 8 , Zero Std: 0 , Max Cor: 0.685
#>
12 <R=0.685,thr=0.600>, Top: 2< 1 >[Fa= 15 ]( 2 , 2 , 15 ),<|><>Tot Used: 28 , Added: 2 , Zero Std: 0 , Max Cor: 0.607
#>
13 <R=0.607,thr=0.600>, Top: 1< 1 >[Fa= 15 ]( 1 , 1 , 15 ),<|><>Tot Used: 28 , Added: 1 , Zero Std: 0 , Max Cor: 0.580
#>
14 <R=0.580,thr=0.500>, Top: 1< 1 >[Fa= 16 ]( 1 , 1 , 15 ),<|><>Tot Used: 28 , Added: 1 , Zero Std: 0 , Max Cor: 0.500
#>
15 <R=0.500,thr=0.400>, Top: 9< 1 >[Fa= 18 ]( 8 , 11 , 16 ),<|><>Tot Used: 32 , Added: 11 , Zero Std: 0 , Max Cor: 0.573
#>
16 <R=0.573,thr=0.500>, Top: 2< 1 >[Fa= 18 ]( 2 , 2 , 18 ),<|><>Tot Used: 32 , Added: 2 , Zero Std: 0 , Max Cor: 0.548
#>
17 <R=0.548,thr=0.500>, Top: 1< 1 >[Fa= 18 ]( 1 , 1 , 18 ),<|><>Tot Used: 32 , Added: 1 , Zero Std: 0 , Max Cor: 0.630
#>
18 <R=0.630,thr=0.600>, Top: 1< 1 >[Fa= 19 ]( 1 , 1 , 18 ),<|><>Tot Used: 32 , Added: 1 , Zero Std: 0 , Max Cor: 0.493
#>
19 <R=0.493,thr=0.400>, Top: 5< 1 >[Fa= 20 ]( 5 , 6 , 19 ),<|><>Tot Used: 32 , Added: 6 , Zero Std: 0 , Max Cor: 0.512
#>
20 <R=0.512,thr=0.500>, Top: 1< 1 >[Fa= 21 ]( 1 , 1 , 20 ),<|><>Tot Used: 32 , Added: 1 , Zero Std: 0 , Max Cor: 0.469
#>
21 <R=0.469,thr=0.400>, Top: 7< 1 >[Fa= 22 ]( 4 , 4 , 21 ),<|><>Tot Used: 32 , Added: 4 , Zero Std: 0 , Max Cor: 0.501
#>
22 <R=0.501,thr=0.500>, Top: 1< 1 >[Fa= 22 ]( 1 , 1 , 22 ),<|><>Tot Used: 32 , Added: 1 , Zero Std: 0 , Max Cor: 0.488
#>
23 <R=0.488,thr=0.400>, Top: 2< 1 >[Fa= 22 ]( 2 , 2 , 22 ),<|><>Tot Used: 32 , Added: 2 , Zero Std: 0 , Max Cor: 0.398
#>
24 <R=0.398,thr=0.400>
#>
[ 24 ], 0.3981401 Decor Dimension: 32 Nused: 32 . Cor to Base: 24 , ABase: 32 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
515156
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
6371
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
1.39
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
1.3
varratio <- attr(DEdataframe,"VarRatio")
pander::pander(tail(varratio))
| La_SE_perimeter | La_SE_area | La_mean_area | La_worst_radius | La_worst_area | La_mean_radius |
|---|---|---|---|---|---|
| 0.0226 | 0.0224 | 0.0151 | 0.0124 | 0.00986 | 0.00771 |
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPLTM <- attr(DEdataframe,"UPLTM")
gplots::heatmap.2(1.0*(abs(UPLTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
Displaying the features associations
par(op)
clustable <- c("To many variables")
transform <- attr(DEdataframe,"UPLTM") != 0
tnames <- colnames(transform)
colnames(transform) <- str_remove_all(colnames(transform),"La_")
transform <- abs(transform*cor(dataframe[,rownames(transform)])) # The weights are proportional to the observed correlation
fscore <- attr(DEdataframe,"fscore")
VertexSize <- fscore # The size depends on the variable independence relevance (fscore)
names(VertexSize) <- str_remove_all(names(VertexSize),"La_")
VertexSize <- 10*(VertexSize-min(VertexSize))/(max(VertexSize)-min(VertexSize)) # Normalization
VertexSize <- VertexSize[rownames(transform)]
rsum <- apply(1*(transform !=0),1,sum) + 0.01*VertexSize + 0.001*varratio[tnames]
csum <- apply(1*(transform !=0),2,sum) + 0.01*VertexSize + 0.001*varratio[tnames]
ntop <- min(10,length(rsum))
topfeatures <- unique(c(names(rsum[order(-rsum)])[1:ntop],names(csum[order(-csum)])[1:ntop]))
rtrans <- transform[topfeatures,]
csum <- (apply(1*(rtrans !=0),2,sum) > 1*(colnames(rtrans) %in% topfeatures))
rtrans <- rtrans[,csum]
topfeatures <- unique(c(topfeatures,colnames(rtrans)))
print(ncol(transform))
[1] 32
transform <- transform[topfeatures,topfeatures]
print(ncol(transform))
[1] 28
if (ncol(transform)>100)
{
csum <- apply(1*(transform !=0),1,sum)
csum <- csum[csum > 1]
csum <- csum[order(-csum)]
tpsum <- min(20,length(csum))
trsum <- rownames(transform)[rownames(transform) %in% names(csum[1:tpsum])]
rtrans <- transform[trsum,]
topfeatures <- unique(c(rownames(rtrans),colnames(rtrans)))
transform <- transform[topfeatures,topfeatures]
if (nrow(transform) > 100)
{
csum <- apply(1*(rtrans != 0 ),2,sum)
csum <- csum[csum > 1]
csum <- csum[order(-csum)]
tpsum <- min(80,length(csum))
csum <- rownames(transform)[rownames(transform) %in% names(csum[1:tpsum])]
csum <- unique(c(trsum,csum))
transform <- transform[csum,csum]
}
print(ncol(transform))
}
if (ncol(transform) < 150)
{
gplots::heatmap.2(transform,
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Red Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
VertexSize <- VertexSize[colnames(transform)]
gr <- graph_from_adjacency_matrix(transform,mode = "directed",diag = FALSE,weighted=TRUE)
gr$layout <- layout_with_fr
fc <- cluster_optimal(gr)
plot(fc, gr,
edge.width = 2*E(gr)$weight,
vertex.size=VertexSize,
edge.arrow.size=0.5,
edge.arrow.width=0.5,
vertex.label.cex=(0.15+0.05*VertexSize),
vertex.label.dist=0.5 + 0.05*VertexSize,
main="Top Feature Association")
varratios <- varratio
fscores <- fscore
names(varratios) <- str_remove_all(names(varratios),"La_")
names(fscores) <- str_remove_all(names(fscores),"La_")
dc <- getLatentCoefficients(DEdataframe)
theCharformulas <- attr(dc,"LatentCharFormulas")
clustable <- as.data.frame(cbind(Variable=fc$names,
Formula=as.character(theCharformulas[paste("La_",fc$names,sep="")]),
Class=fc$membership,
ResidualVariance=round(varratios[fc$names],3),
Fscore=round(fscores[fc$names],3)
)
)
rownames(clustable) <- str_replace_all(rownames(clustable),"__","_")
clustable$Variable <- NULL
clustable$Class <- as.integer(clustable$Class)
clustable$ResidualVariance <- as.numeric(clustable$ResidualVariance)
clustable$Fscore <- as.numeric(clustable$Fscore)
clustable <- clustable[order(-clustable$Fscore),]
clustable <- clustable[order(clustable$Class),]
clustable <- clustable[clustable$Fscore >= -1,]
topv <- min(50,nrow(clustable))
clustable <- clustable[1:topv,]
}
pander::pander(clustable)
| Formula | Class | ResidualVariance | Fscore | |
|---|---|---|---|---|
| mean_perimeter | NA | 1 | 1.000 | 9 |
| mean_radius | + mean_radius - (0.149)mean_perimeter | 1 | 0.008 | 9 |
| SE_radius | + (4.16e-03)mean_perimeter + SE_radius - (9.27e-03)worst_perimeter | 1 | 0.501 | 3 |
| worst_perimeter | - (1.298)mean_perimeter + worst_perimeter | 1 | 0.151 | 2 |
| mean_area | - (83.732)mean_radius - (4.090)mean_perimeter + mean_area | 1 | 0.015 | 0 |
| worst_radius | - (1.834)mean_radius + (0.273)mean_perimeter + worst_radius - (0.147)worst_perimeter | 1 | 0.012 | 0 |
| worst_compactness | + (0.405)mean_radius - (0.060)mean_perimeter + worst_compactness | 2 | 0.533 | 5 |
| mean_fractaldim | + (0.019)mean_radius - (2.69e-03)mean_perimeter + mean_fractaldim | 2 | 0.328 | 5 |
| mean_concavepoints | + (0.029)mean_radius - (5.65e-03)mean_perimeter + mean_concavepoints - (1.846)mean_fractaldim | 2 | 0.161 | 0 |
| mean_symmetry | - (5.03e-03)mean_radius + (3.22e-04)mean_perimeter + mean_symmetry - (2.774)mean_fractaldim | 2 | 0.603 | 0 |
| mean_smoothness | - (0.012)mean_radius + (2.32e-03)mean_perimeter + mean_smoothness - (0.411)mean_concavepoints - (0.521)mean_fractaldim | 2 | 0.201 | -1 |
| worst_symmetry | - (0.094)mean_radius + (0.014)mean_perimeter - (2.064)mean_symmetry - (0.232)worst_compactness + worst_symmetry | 2 | 0.281 | -1 |
| worst_fractaldim | - (0.029)mean_radius + (4.31e-03)mean_perimeter - (2.460)mean_fractaldim - (0.071)worst_compactness + worst_fractaldim | 2 | 0.131 | -1 |
| SE_fractaldim | + (4.86e-04)mean_radius - (1.01e-04)mean_perimeter - (0.184)mean_fractaldim + SE_fractaldim | 3 | 0.532 | 3 |
| SE_compactness | + SE_compactness - (7.818)SE_fractaldim | 3 | 0.272 | 2 |
| worst_concavity | - (0.830)worst_compactness + worst_concavity | 3 | 0.292 | 1 |
par(op)
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after ILAA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.3981401
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
topvars <- univariate_BinEnsemble(dataframe,outcome)
lso <- LASSO_MIN(formula(paste(outcome,"~.")),dataframe,family="binomial")
topvars <- unique(c(names(topvars),lso$selectedfeatures))
pander::pander(head(topvars))
mean_radius, worst_radius, pnodes, tsize, SE_perimeter and SE_radius
# names(topvars)
#if (nrow(dataframe) < 1000)
#{
datasetframe.umap = umap(scale(dataframe[1:numsub,topvars]),n_components=2)
# datasetframe.umap = umap(dataframe[1:numsub,varlist],n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
#}
varlistcV <- names(varratio[varratio >= 0.01])
topvars <- univariate_BinEnsemble(DEdataframe[,varlistcV],outcome)
lso <- LASSO_MIN(formula(paste(outcome,"~.")),DEdataframe[,varlistcV],family="binomial")
topvars <- unique(c(names(topvars),lso$selectedfeatures))
pander::pander(head(topvars))
tsize, mean_perimeter, La_worst_fractaldim, La_SE_symmetry, La_mean_smoothness and La_mean_fractaldim
varlistcV <- varlistcV[varlistcV != outcome]
# DEdataframe[,outcome] <- as.numeric(DEdataframe[,outcome])
#if (nrow(dataframe) < 1000)
#{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,topvars]),n_components=2)
# datasetframe.umap = umap(DEdataframe[1:numsub,varlistcV],n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After ILAA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
#}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##top variables
topvar <- c(1:length(varlist)) <= TopVariables
tableRaw <- univarRAW$orderframe[topvar,univariate_columns]
pander::pander(tableRaw)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| tsize | 3.47 | 2.03 | 2.64 | 1.86 | 1.11e-03 | 0.666 |
| pnodes | 4.87 | 6.02 | 2.63 | 5.21 | 6.25e-09 | 0.650 |
| worst_radius | 22.67 | 4.70 | 20.35 | 4.08 | 3.68e-01 | 0.647 |
| worst_perimeter | 151.33 | 32.42 | 135.34 | 26.85 | 5.71e-01 | 0.645 |
| mean_area | 1081.98 | 397.26 | 888.40 | 310.85 | 1.26e-01 | 0.645 |
| worst_area | 1635.77 | 703.15 | 1317.95 | 550.94 | 2.72e-01 | 0.643 |
| mean_perimeter | 121.10 | 22.91 | 110.02 | 19.19 | 4.72e-01 | 0.641 |
| mean_radius | 18.33 | 3.37 | 16.70 | 2.91 | 3.12e-01 | 0.639 |
| SE_perimeter | 4.73 | 2.21 | 3.81 | 1.80 | 6.37e-02 | 0.634 |
| SE_area | 81.97 | 53.36 | 61.22 | 37.72 | 6.46e-02 | 0.632 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
pander::pander(finalTable)
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| tsize | 3.47174 | 2.02985 | 2.63846 | 1.85507 | 0.00111 | 0.666 |
| La_worst_area | 409.10338 | 59.42237 | 440.08923 | 60.65702 | 0.52089 | 0.653 |
| La_SE_symmetry | -0.00356 | 0.00448 | -0.00579 | 0.00572 | 0.23435 | 0.645 |
| mean_perimeter | 121.09522 | 22.91019 | 110.02231 | 19.18940 | 0.47168 | 0.641 |
| La_worst_fractaldim | -0.09538 | 0.00580 | -0.09896 | 0.00862 | 0.93594 | 0.639 |
| La_mean_smoothness | 0.09341 | 0.00508 | 0.09102 | 0.00555 | 0.90681 | 0.626 |
| La_mean_fractaldim | 0.08522 | 0.00324 | 0.08699 | 0.00471 | 0.35987 | 0.600 |
| La_worst_perimeter | -5.80502 | 11.69785 | -7.42934 | 11.47409 | 0.02995 | 0.577 |
| La_SE_concavity | -0.01260 | 0.00681 | -0.01037 | 0.00581 | 0.22238 | 0.573 |
| La_worst_concavity | 0.14327 | 0.09717 | 0.11997 | 0.08164 | 0.81386 | 0.572 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 4.79 | 29 | 0.906 |
theCharformulas <- attr(dc,"LatentCharFormulas")
topvar <- rownames(tableRaw)
finalTable <- rbind(finalTable,tableRaw[topvar[!(topvar %in% topLAvar)],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- theCharformulas[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
finalTable$varratio <- varratio[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores","varratio")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | varratio | |
|---|---|---|---|---|---|---|---|---|---|---|
| tsize | NA | 3.47e+00 | 2.03e+00 | 2.64e+00 | 1.86e+00 | 1.11e-03 | 0.666 | 0.666 | 1 | 1.00000 |
| La_worst_area | + (2.56e+02)mean_radius - (4.838)mean_perimeter - (2.009)mean_area - (1.39e+02)worst_radius + (0.035)worst_perimeter + worst_area | 4.09e+02 | 5.94e+01 | 4.40e+02 | 6.07e+01 | 5.21e-01 | 0.653 | 0.643 | -2 | 0.00986 |
| pnodes | NA | 4.87e+00 | 6.02e+00 | 2.63e+00 | 5.21e+00 | 6.25e-09 | 0.650 | 0.650 | NA | NA |
| worst_radius | NA | 2.27e+01 | 4.70e+00 | 2.03e+01 | 4.08e+00 | 3.68e-01 | 0.647 | 0.647 | NA | NA |
| worst_perimeter | NA | 1.51e+02 | 3.24e+01 | 1.35e+02 | 2.68e+01 | 5.71e-01 | 0.645 | 0.645 | NA | NA |
| La_SE_symmetry | - (2.99e-03)mean_radius + (5.30e-04)mean_perimeter + (3.57e-03)mean_symmetry - (0.376)mean_fractaldim + SE_symmetry - (2.978)SE_fractaldim - (3.80e-03)worst_compactness - (0.099)worst_symmetry + (0.375)worst_fractaldim | -3.56e-03 | 4.48e-03 | -5.79e-03 | 5.72e-03 | 2.34e-01 | 0.645 | 0.504 | -4 | 0.27828 |
| mean_area | NA | 1.08e+03 | 3.97e+02 | 8.88e+02 | 3.11e+02 | 1.26e-01 | 0.645 | 0.645 | NA | NA |
| worst_area | NA | 1.64e+03 | 7.03e+02 | 1.32e+03 | 5.51e+02 | 2.72e-01 | 0.643 | 0.643 | NA | NA |
| mean_perimeter | NA | 1.21e+02 | 2.29e+01 | 1.10e+02 | 1.92e+01 | 4.72e-01 | 0.641 | 0.641 | 9 | 1.00000 |
| La_worst_fractaldim | - (0.029)mean_radius + (4.31e-03)mean_perimeter - (2.460)mean_fractaldim - (0.071)worst_compactness + worst_fractaldim | -9.54e-02 | 5.80e-03 | -9.90e-02 | 8.62e-03 | 9.36e-01 | 0.639 | 0.583 | -1 | 0.13072 |
| mean_radius | NA | 1.83e+01 | 3.37e+00 | 1.67e+01 | 2.91e+00 | 3.12e-01 | 0.639 | 0.639 | NA | NA |
| SE_perimeter | NA | 4.73e+00 | 2.21e+00 | 3.81e+00 | 1.80e+00 | 6.37e-02 | 0.634 | 0.634 | NA | NA |
| SE_area | NA | 8.20e+01 | 5.34e+01 | 6.12e+01 | 3.77e+01 | 6.46e-02 | 0.632 | 0.632 | NA | NA |
| La_mean_smoothness | - (0.012)mean_radius + (2.32e-03)mean_perimeter + mean_smoothness - (0.411)mean_concavepoints - (0.521)mean_fractaldim | 9.34e-02 | 5.08e-03 | 9.10e-02 | 5.55e-03 | 9.07e-01 | 0.626 | 0.518 | -1 | 0.20052 |
| La_mean_fractaldim | + (0.019)mean_radius - (2.69e-03)mean_perimeter + mean_fractaldim | 8.52e-02 | 3.24e-03 | 8.70e-02 | 4.71e-03 | 3.60e-01 | 0.600 | 0.615 | 5 | 0.32801 |
| La_worst_perimeter | - (1.298)mean_perimeter + worst_perimeter | -5.81e+00 | 1.17e+01 | -7.43e+00 | 1.15e+01 | 2.99e-02 | 0.577 | 0.645 | 2 | 0.15076 |
| La_SE_concavity | - (0.766)SE_compactness + SE_concavity - (1.245)SE_concavepoints + (0.455)SE_fractaldim + (0.064)worst_compactness - (0.077)worst_concavity | -1.26e-02 | 6.81e-03 | -1.04e-02 | 5.81e-03 | 2.22e-01 | 0.573 | 0.478 | -2 | 0.10094 |
| La_worst_concavity | - (0.830)worst_compactness + worst_concavity | 1.43e-01 | 9.72e-02 | 1.20e-01 | 8.16e-02 | 8.14e-01 | 0.572 | 0.492 | 1 | 0.29167 |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,scale. = TRUE,tol=0.01) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)-1)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 68 | 23 |
| 1 | 9 | 37 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.766 | 0.687 | 0.834 |
| 3 | se | 0.804 | 0.661 | 0.906 |
| 4 | sp | 0.747 | 0.645 | 0.833 |
| 6 | diag.or | 12.155 | 5.100 | 28.966 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe[,c(outcome,varlistcV)],control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="ILAA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 89 | 2 |
| 1 | 33 | 13 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.745 | 0.663 | 0.815 |
| 3 | se | 0.283 | 0.160 | 0.435 |
| 4 | sp | 0.978 | 0.923 | 0.997 |
| 6 | diag.or | 17.530 | 3.753 | 81.883 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 86 | 5 |
| 1 | 27 | 19 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.766 | 0.687 | 0.834 |
| 3 | se | 0.413 | 0.270 | 0.568 |
| 4 | sp | 0.945 | 0.876 | 0.982 |
| 6 | diag.or | 12.104 | 4.128 | 35.493 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 73 | 18 |
| 1 | 11 | 35 |
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.788 | 0.710 | 0.853 |
| 3 | se | 0.761 | 0.612 | 0.874 |
| 4 | sp | 0.802 | 0.706 | 0.878 |
| 6 | diag.or | 12.904 | 5.507 | 30.236 |
par(op)